Coming out from the growth kinetics, the Gompertz model has been developed and considered as the best one for simulating the biogas production from anaerobic digestion. However, the model has failed to describe the starting point of the process, and no-sense of lag phase constant has been pointed out. Thus, the goal of this study is to develop a new kinetic model of biogas production with meani...

In different tasks such as adaptive background modelling, the Singular Value Decomposition (SVD) has to be applied in running fashion. Typically, this happens when the SVD is used in a sliding spatial or temporal data window. Each time the window moves on, the SVD should be calculated in the batch mode from scratch, or re-calculated using the previous solution. When the data matrix is relativel...

Simultaneous saccharification and fermentation (SSF) studies of steam exploded and alkali pretreated different leafy biomass were accomplished by recombinant Clostridium thermocellum hydrolytic enzymes and fermentative microbes for bioethanol production. The recombinant C. thermocellum GH5 cellulase and GH43 hemicellulase genes expressed in Escherichia coli cells were grown in repetitive batch ...

This paper reports recent advances in batch mode micro-electro-discharge machining (μEDM) for high precision micromachining of metal alloys such as stainless steel. High-aspect-ratio silicon microstructures with fine feature sizes formed by deep reactive ion etching are used as cutting tools. To machine workpiece features with widths 10 μm, a silicon dioxide coating is necessary to passivate th...

Interactive and batch tasks typically have aperiodic random demands and arrival patterns. Generally, interactive tasks are assigned high priority for high responsiveness. Batch tasks with less timing criticality are scheduled in background. Unfortunately, most real-time DVS algorithms focus only on the real-time task workload and timing constraints in determining the operating power-optimized c...

This paper highlights the crucial role that modern machine learning techniques can play in the optimization of treatment strategies for patients with chronic disorders. In particular, we focus on the task of optimizing a deep-brain stimulation strategy for the treatment of epilepsy. The challenge is to choose which stimulation action to apply, as a function of the observed EEG signal, so as to ...

We introduce the Optimal Sample Selection (OSS) meta-algorithm for solving discrete-time Optimal Control problems. This meta-algorithm maps the problem of finding a near-optimal closed-loop policy to the identification of a small set of one-step system transitions, leading to high-quality policies when used as input of a batch-mode Reinforcement Learning (RL) algorithm. We detail a particular i...

The production of value-added chemicals from the bioconversion of lignocellulose biomass has been considered a promising venture. In this study, microwave, alkali-pretreated empty fruit bunch (EFB) was used as the substrate, utilizing pelletized filamentous Rhizopus oryzae NRRL 395 and cellulolytic enzymes for lactic acid production in a fed-batch simultaneous saccharification and fermentation ...